421 lines
16 KiB
Python
421 lines
16 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import copy
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import torch
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import numpy as np
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import torch.nn.functional as F
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from typing import Any, List, Tuple, Optional
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from funasr.register import tables
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from funasr.train_utils.device_funcs import to_device
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.utils.load_utils import load_audio_text_image_video
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words
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try:
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import jieba
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except:
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pass
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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@tables.register("model_classes", "CTTransformer")
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class CTTransformer(torch.nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
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https://arxiv.org/pdf/2003.01309.pdf
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"""
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def __init__(
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self,
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encoder: str = None,
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encoder_conf: dict = None,
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vocab_size: int = -1,
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punc_list: list = None,
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punc_weight: list = None,
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embed_unit: int = 128,
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att_unit: int = 256,
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dropout_rate: float = 0.5,
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ignore_id: int = -1,
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sos: int = 1,
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eos: int = 2,
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sentence_end_id: int = 3,
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**kwargs,
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):
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super().__init__()
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punc_size = len(punc_list)
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if punc_weight is None:
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punc_weight = [1] * punc_size
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self.embed = torch.nn.Embedding(vocab_size, embed_unit)
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encoder_class = tables.encoder_classes.get(encoder)
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encoder = encoder_class(**encoder_conf)
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self.decoder = torch.nn.Linear(att_unit, punc_size)
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self.encoder = encoder
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self.punc_list = punc_list
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self.punc_weight = punc_weight
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self.ignore_id = ignore_id
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self.sos = sos
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self.eos = eos
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self.sentence_end_id = sentence_end_id
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self.jieba_usr_dict = None
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if kwargs.get("jieba_usr_dict", None) is not None:
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jieba.load_userdict(kwargs["jieba_usr_dict"])
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self.jieba_usr_dict = jieba
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def punc_forward(self, text: torch.Tensor, text_lengths: torch.Tensor, **kwargs):
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(text)
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# mask = self._target_mask(input)
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h, _, _ = self.encoder(x, text_lengths)
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y = self.decoder(h)
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return y, None
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def with_vad(self):
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return False
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def score(self, y: torch.Tensor, state: Any, x: torch.Tensor) -> Tuple[torch.Tensor, Any]:
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"""Score new token.
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Args:
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y (torch.Tensor): 1D torch.int64 prefix tokens.
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state: Scorer state for prefix tokens
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x (torch.Tensor): encoder feature that generates ys.
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Returns:
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tuple[torch.Tensor, Any]: Tuple of
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torch.float32 scores for next token (vocab_size)
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and next state for ys
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"""
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y = y.unsqueeze(0)
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h, _, cache = self.encoder.forward_one_step(
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self.embed(y), self._target_mask(y), cache=state
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)
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h = self.decoder(h[:, -1])
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logp = h.log_softmax(dim=-1).squeeze(0)
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return logp, cache
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def batch_score(
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self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
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) -> Tuple[torch.Tensor, List[Any]]:
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"""Score new token batch.
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Args:
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ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
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states (List[Any]): Scorer states for prefix tokens.
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xs (torch.Tensor):
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The encoder feature that generates ys (n_batch, xlen, n_feat).
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Returns:
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tuple[torch.Tensor, List[Any]]: Tuple of
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batchfied scores for next token with shape of `(n_batch, vocab_size)`
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and next state list for ys.
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"""
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# merge states
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n_batch = len(ys)
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n_layers = len(self.encoder.encoders)
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if states[0] is None:
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batch_state = None
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else:
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# transpose state of [batch, layer] into [layer, batch]
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batch_state = [
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torch.stack([states[b][i] for b in range(n_batch)]) for i in range(n_layers)
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]
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# batch decoding
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h, _, states = self.encoder.forward_one_step(
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self.embed(ys), self._target_mask(ys), cache=batch_state
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)
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h = self.decoder(h[:, -1])
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logp = h.log_softmax(dim=-1)
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# transpose state of [layer, batch] into [batch, layer]
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state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
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return logp, state_list
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def nll(
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self,
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text: torch.Tensor,
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punc: torch.Tensor,
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text_lengths: torch.Tensor,
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punc_lengths: torch.Tensor,
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max_length: Optional[int] = None,
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vad_indexes: Optional[torch.Tensor] = None,
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vad_indexes_lengths: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute negative log likelihood(nll)
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Normally, this function is called in batchify_nll.
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Args:
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text: (Batch, Length)
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punc: (Batch, Length)
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text_lengths: (Batch,)
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max_lengths: int
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"""
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batch_size = text.size(0)
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# For data parallel
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if max_length is None:
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text = text[:, : text_lengths.max()]
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punc = punc[:, : text_lengths.max()]
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else:
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text = text[:, :max_length]
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punc = punc[:, :max_length]
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if self.with_vad():
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# Should be VadRealtimeTransformer
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assert vad_indexes is not None
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y, _ = self.punc_forward(text, text_lengths, vad_indexes)
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else:
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# Should be TargetDelayTransformer,
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y, _ = self.punc_forward(text, text_lengths)
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# Calc negative log likelihood
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# nll: (BxL,)
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if self.training == False:
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_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
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from sklearn.metrics import f1_score
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f1_score = f1_score(
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punc.view(-1).detach().cpu().numpy(),
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indices.squeeze(-1).detach().cpu().numpy(),
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average="micro",
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)
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nll = torch.Tensor([f1_score]).repeat(text_lengths.sum())
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return nll, text_lengths
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else:
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self.punc_weight = self.punc_weight.to(punc.device)
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nll = F.cross_entropy(
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y.view(-1, y.shape[-1]),
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punc.view(-1),
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self.punc_weight,
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reduction="none",
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ignore_index=self.ignore_id,
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)
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# nll: (BxL,) -> (BxL,)
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if max_length is None:
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nll.masked_fill_(make_pad_mask(text_lengths).to(nll.device).view(-1), 0.0)
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else:
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nll.masked_fill_(
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make_pad_mask(text_lengths, maxlen=max_length + 1).to(nll.device).view(-1),
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0.0,
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)
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# nll: (BxL,) -> (B, L)
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nll = nll.view(batch_size, -1)
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return nll, text_lengths
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def forward(
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self,
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text: torch.Tensor,
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punc: torch.Tensor,
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text_lengths: torch.Tensor,
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punc_lengths: torch.Tensor,
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vad_indexes: Optional[torch.Tensor] = None,
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vad_indexes_lengths: Optional[torch.Tensor] = None,
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):
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nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths, vad_indexes=vad_indexes)
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ntokens = y_lengths.sum()
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loss = nll.sum() / ntokens
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stats = dict(loss=loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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loss, stats, weight = force_gatherable((loss, stats, ntokens), loss.device)
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return loss, stats, weight
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def inference(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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assert len(data_in) == 1
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text = load_audio_text_image_video(data_in, data_type=kwargs.get("kwargs", "text"))[0]
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vad_indexes = kwargs.get("vad_indexes", None)
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# text = data_in[0]
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# text_lengths = data_lengths[0] if data_lengths is not None else None
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split_size = kwargs.get("split_size", 20)
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tokens = split_words(text, jieba_usr_dict=self.jieba_usr_dict)
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tokens_int = tokenizer.encode(tokens)
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mini_sentences = split_to_mini_sentence(tokens, split_size)
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mini_sentences_id = split_to_mini_sentence(tokens_int, split_size)
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assert len(mini_sentences) == len(mini_sentences_id)
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cache_sent = []
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cache_sent_id = torch.from_numpy(np.array([], dtype="int32"))
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new_mini_sentence = ""
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new_mini_sentence_punc = []
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cache_pop_trigger_limit = 200
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results = []
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meta_data = {}
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punc_array = None
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for mini_sentence_i in range(len(mini_sentences)):
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mini_sentence = mini_sentences[mini_sentence_i]
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mini_sentence_id = mini_sentences_id[mini_sentence_i]
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mini_sentence = cache_sent + mini_sentence
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mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
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data = {
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"text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
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"text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype="int32")),
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}
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data = to_device(data, kwargs["device"])
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# y, _ = self.wrapped_model(**data)
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y, _ = self.punc_forward(**data)
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_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
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punctuations = torch.squeeze(indices, dim=1)
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assert punctuations.size()[0] == len(mini_sentence)
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# Search for the last Period/QuestionMark as cache
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if mini_sentence_i < len(mini_sentences) - 1:
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sentenceEnd = -1
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last_comma_index = -1
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for i in range(len(punctuations) - 2, 1, -1):
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if (
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self.punc_list[punctuations[i]] == "。"
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or self.punc_list[punctuations[i]] == "？"
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):
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sentenceEnd = i
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break
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if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
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last_comma_index = i
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if (
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sentenceEnd < 0
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and len(mini_sentence) > cache_pop_trigger_limit
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and last_comma_index >= 0
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):
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# The sentence it too long, cut off at a comma.
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sentenceEnd = last_comma_index
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punctuations[sentenceEnd] = self.sentence_end_id
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cache_sent = mini_sentence[sentenceEnd + 1 :]
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cache_sent_id = mini_sentence_id[sentenceEnd + 1 :]
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mini_sentence = mini_sentence[0 : sentenceEnd + 1]
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punctuations = punctuations[0 : sentenceEnd + 1]
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# if len(punctuations) == 0:
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# continue
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punctuations_np = punctuations.cpu().numpy()
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new_mini_sentence_punc += [int(x) for x in punctuations_np]
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words_with_punc = []
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for i in range(len(mini_sentence)):
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if (
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i == 0
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or self.punc_list[punctuations[i - 1]] == "。"
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or self.punc_list[punctuations[i - 1]] == "？"
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) and len(mini_sentence[i][0].encode()) == 1:
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mini_sentence[i] = mini_sentence[i].capitalize()
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if i == 0:
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if len(mini_sentence[i][0].encode()) == 1:
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mini_sentence[i] = " " + mini_sentence[i]
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if i > 0:
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if (
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len(mini_sentence[i][0].encode()) == 1
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and len(mini_sentence[i - 1][0].encode()) == 1
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):
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mini_sentence[i] = " " + mini_sentence[i]
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words_with_punc.append(mini_sentence[i])
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if self.punc_list[punctuations[i]] != "_":
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punc_res = self.punc_list[punctuations[i]]
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if len(mini_sentence[i][0].encode()) == 1:
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if punc_res == "，":
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punc_res = ","
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elif punc_res == "。":
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punc_res = "."
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elif punc_res == "？":
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punc_res = "?"
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words_with_punc.append(punc_res)
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new_mini_sentence += "".join(words_with_punc)
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# Add Period for the end of the sentence
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new_mini_sentence_out = new_mini_sentence
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new_mini_sentence_punc_out = new_mini_sentence_punc
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if mini_sentence_i == len(mini_sentences) - 1:
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if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、":
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new_mini_sentence_out = new_mini_sentence[:-1] + "。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [
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self.sentence_end_id
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]
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elif new_mini_sentence[-1] == ",":
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new_mini_sentence_out = new_mini_sentence[:-1] + "."
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [
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self.sentence_end_id
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]
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elif (
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new_mini_sentence[-1] != "。"
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and new_mini_sentence[-1] != "？"
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and len(new_mini_sentence[-1].encode()) != 1
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):
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new_mini_sentence_out = new_mini_sentence + "。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [
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self.sentence_end_id
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]
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if len(punctuations):
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punctuations[-1] = 2
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elif (
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new_mini_sentence[-1] != "."
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and new_mini_sentence[-1] != "?"
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and len(new_mini_sentence[-1].encode()) == 1
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):
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new_mini_sentence_out = new_mini_sentence + "."
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [
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self.sentence_end_id
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]
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if len(punctuations):
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punctuations[-1] = 2
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# keep a punctuations array for punc segment
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if punc_array is None:
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punc_array = punctuations
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else:
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punc_array = torch.cat([punc_array, punctuations], dim=0)
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# post processing when using word level punc model
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if self.jieba_usr_dict is not None:
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punc_array = punc_array.reshape(-1)
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len_tokens = len(tokens)
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new_punc_array = copy.copy(punc_array).tolist()
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# for i, (token, punc_id) in enumerate(zip(tokens[::-1], punc_array.tolist()[::-1])):
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for i, token in enumerate(tokens[::-1]):
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if "\u0e00" <= token[0] <= "\u9fa5": # ignore en words
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if len(token) > 1:
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num_append = len(token) - 1
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ind_append = len_tokens - i - 1
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for _ in range(num_append):
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new_punc_array.insert(ind_append, 1)
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punc_array = torch.tensor(new_punc_array)
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result_i = {"key": key[0], "text": new_mini_sentence_out, "punc_array": punc_array}
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results.append(result_i)
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return results, meta_data
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def export(self, **kwargs):
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from .export_meta import export_rebuild_model
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models = export_rebuild_model(model=self, **kwargs)
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return models
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